Three Use Cases for Interactive Data Discovery and Predictive Analytics

What Are Interactive Data Discovery and Predictive Analytics? Interactive data discovery and predictive analytics technologies allow business users and analysts to identify important trends and relationships in data sets and drill down into questions from any angle. They also allow business users to collaborate with specialists to create, refine and select the best predictive models.

A comprehensive interactive solution such as that provided by SAS should include an easy-to-use interface that allows even nontechnical users to visually explore data, create models and share results—empowering users to make smarter business decisions, faster.

With the right solution, users can explore data and create analytical models using a variety of descriptive and predictive analytics techniques, all from a single interface.

These abilities empower users to innovate in new and smarter ways. They can analyze data without preconceived notions, test new ideas and take a bottom-up approach through simpler, faster ways to visualize important findings. They can then share these findings easily with teammates.

To see just how interactive data discovery and predictive analytics solve everyday business problems, we can examine the life of three fictional employees that we’ll call Bekha, Jorge and Devon.

Interactive Data Discovery Illuminates Customer Churn in Marketing Bekha is a marketing manager at a large European retailer who has been tasked with researching the problem of customer churn. In the past, Bekha only had access to historical data, seeing monthly reports about numbers of customers that had already stopped buying from her company’s stores.

With interactive data discovery, Bekha can gain new insights from that data by exploring it in different ways. This exploration allows her to identify trends and relationships that were not apparent before, helping her to find new variables associated with customer churn. She can quickly drill down into those variables and ask new questions.

The solution is even more powerful when interactive data discovery is combined with predictive analytics. Based on relationships and trends she has identified during discovery, Bekha can use logistic regression or decision tree techniques to predict the probability of churn for each individual customer. She can then take specific, targeted actions to retain those customers.

Furthermore, she can use the output of the model to visualize the causes of churn and how to prevent it, and can quickly share her results with colleagues.

Better Customer Modeling Reduces Risk in Financial Services Jorge leads a team of risk analysts to support a mortgage firm that targets underserved populations in North America. Their traditional credit risk models were reasonably effective assessment tools, but they were not designed to predict mortgage defaults.

But their new data discovery and analytics solution lets Jorge’s team more precisely understand customers who default on their mortgages. The solution analyzes data from multiple sources—not just payment history—to create more complete customer profiles. These profiles and the predictive analytics capabilities help Jorge and his team identify customers that are at risk of default and allow the company to set appropriate credit limits for new customers.

While providing much deeper insight into customers, the solution strictly adheres to data governance policies. Integrated governance not only guarantees that Jorge’s results are reliable by ensuring data integrity, it also helps protect personally identifiable information.

Self-Service Analytics in Engineering Enables Proactive Maintenance Devon is chief engineer for a wind and solar power company with large installations all over the western United States. In the past, he could only react to equipment failures by dispatching a technician to repair or replace equipment.

Now, with self-service analytics, he can explore overall equipment efficiency and investigate factors that contribute to equipment failure. He does this by creating a decision tree to explain past failures and predict future failure. The interactive capabilities allow him to grow and prune the tree to the desired level of detail, and then compare his predictive model to a logistic regression model to see which is most effective. He can run the model against very large data sets, even when new systems come online and he needs to scale rapidly.

It Is Time to Empower Business Users Interactive data discovery and analytics put the power of big data in the hands of business users and analysts, giving them the insights they need to make smarter, data-driven business decisions.

SAS Visual Analytics and Visual Statistics let users visually explore and analyze data in new and collaborative ways while tapping into powerful in-memory technologies for faster analytic computations and discoveries.